What is he best known for?
The fields of study he is best known for:
- Gene
- DNA
- Artificial intelligence
Burkhard Rost mostly deals with Computational biology, Protein structure, Genetics, Protein secondary structure and Protein structure prediction.
His Computational biology research integrates issues from Proteome, Protein function prediction, Peptide sequence, Sequence analysis and Membrane protein.
The concepts of his Protein structure study are interwoven with issues in Amino acid, Protein–protein interaction, Conserved sequence, Protein folding and Sequence.
His work investigates the relationship between Genetics and topics such as Snap that intersect with problems in Neutral mutation, Supplementary data, Single amino acid and Function.
The Protein secondary structure study combines topics in areas such as Bioinformatics, Artificial neural network, Globular protein, Percentage point and Algorithm.
His Protein structure prediction research is multidisciplinary, incorporating elements of Data mining and Threading.
His most cited work include:
- The Transcriptional Landscape of the Mammalian Genome (2876 citations)
- Prediction of Protein Secondary Structure at Better than 70% Accuracy (2664 citations)
- The PredictProtein server (1381 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Computational biology, Structural genomics, Protein structure, Genetics and Protein secondary structure.
His research integrates issues of Proteome, Bioinformatics, Peptide sequence, Homology and Sequence analysis in his study of Computational biology.
His Structural genomics research includes themes of Domain, Solution structure, Stereochemistry and Protein domain.
Burkhard Rost works mostly in the field of Protein structure, limiting it down to topics relating to Transmembrane domain and, in certain cases, Membrane protein.
Genome, Sequence, In silico, Protein–protein interaction and Genomics are among the areas of Genetics where Burkhard Rost concentrates his study.
His Protein secondary structure study incorporates themes from Artificial neural network, Artificial intelligence, Algorithm and Protein structure prediction.
He most often published in these fields:
- Computational biology (53.49%)
- Structural genomics (32.56%)
- Protein structure (26.74%)
What were the highlights of his more recent work (between 2014-2021)?
- Computational biology (53.49%)
- Artificial intelligence (13.26%)
- Machine learning (6.98%)
In recent papers he was focusing on the following fields of study:
Computational biology, Artificial intelligence, Machine learning, Proteome and Protein structure are his primary areas of study.
His biological study spans a wide range of topics, including Transmembrane protein, Gene, Homology, Sequence and Protein sequencing.
His Proteome research includes elements of Schizosaccharomyces pombe, Proteomics and UniProt.
The study incorporates disciplines such as Evolutionary biology, Protein secondary structure and Protein family in addition to Protein structure.
He focuses mostly in the field of Protein secondary structure, narrowing it down to topics relating to Subcellular localization and, in certain cases, Protein structure and function and Artificial neural network.
His Genome study is associated with Genetics.
Between 2014 and 2021, his most popular works were:
- A draft network of ligand–receptor-mediated multicellular signalling in human (317 citations)
- Better prediction of functional effects for sequence variants (274 citations)
- An expanded evaluation of protein function prediction methods shows an improvement in accuracy (241 citations)
In his most recent research, the most cited papers focused on:
- Gene
- DNA
- Artificial intelligence
His primary areas of investigation include Computational biology, Artificial intelligence, Machine learning, Protein structure and Biochemistry.
His Computational biology research is multidisciplinary, relying on both Genome, Support vector machine, Transmembrane domain and Protein–protein interaction prediction.
His work carried out in the field of Artificial intelligence brings together such families of science as Set and Protein–protein interaction.
His Machine learning study combines topics from a wide range of disciplines, such as Training set, Sequence, Protein function, Focus and Protein Interaction Networks.
His Protein structure study combines topics in areas such as Glycosylation, Endoplasmic reticulum and Data integration.
His study in Deep learning is interdisciplinary in nature, drawing from both Protein secondary structure, Artificial neural network, Target protein, Transfer of learning and Subcellular localization.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
